Biology subjects

Bristy, N. A.

Publications and source records attributed to Bristy, N. A..

4 recordsLinked to original sources

bioRxiv2025

Theoretical estimates on the expected number of mutations for reconstructing clonal lineage trees

Phylogenetics, like many subdisciplines of computational biology, faces a growing challenge of dealing with increasingly large and complicated data sets that have been enabled by ever-improving technologies for sequencing. The issue is particularly acute for studies of somatic evolution, such as of single-cell populations in cancers, where vast single-cell data sets may now identify hundreds of thousands of genetically distinct cells with similar scales of mutations distinguishing them. At the same time, the complexity of the biology of somatic evolution has led to complex phylogeny methods that struggle to scale to even modest data sizes. In this paper, we explore the theoretical and empirical basis for one strategy for managing these large data sets: subsampling mutations to solve the computationally challenging phylogeny problem followed by faster solutions for placement of mutations on a putatively known guide tree. We specifically focus on the fundamental question of determining the number of mutations sufficient to recover the true phylogenetic tree at some level of resolution with high probability. We theoretically analyze variants of several common models that underlie popular tools for building clonal lineage trees. We further evaluate the robustness of these theoretical bounds through simulations of these models, extensions of them, and real biological datasets. The results suggest that modest numbers of mutations suffice to reconstruct clonal trees for typical numbers of clones, supporting the sub-sampling approach as a general strategy for managing the challenges of ever-growing data sets.

cancer biology↗

bioRxiv2025

Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq

MotivationReconstructing clonal lineage trees ("tumor phylogenetics") has become a core tool of cancer genomics. Earlier approaches based on bulk DNA sequencing (DNA-seq) have largely given way to single-cell DNA-seq (scDNA-seq), which offers far greater resolution for clonal substructure. Available data has lagged behind computational theory, though. While single-cell RNA-seq (scRNA-seq) has become widely available, scDNA-seq is still sufficiently costly and technically challenging to preclude routine use on large cohorts. This forces difficult tradeoffs between the limited genome coverage of scRNA-seq, limited availability of scDNA-seq, and limited clonal resolution of bulk DNA-seq. These limitations are especially problematic for studying structural variations and focal copy number variations that are crucial to cancer progression but difficult to observe in RNA-seq. ResultsWe develop a method, TUSV-int, combining advantages of these various genomic technologies by integrating bulk DNA-seq and scRNA-seq data into a single deconvolution and phylogenetic inference computation while allowing for single nucleotide variant (SNV), copy number alteration (CNA) and structural variant (SV) data. We accomplish this by using integer linear programming (ILP) to deconvolve heterogeneous variant types and resolve them into a clonal lineage tree. We demonstrate improved deconvolution performance over comparative methods lacking scRNA-seq data or using more limited variant types. We further demonstrate the power of the method to better resolve clonal structure and mutational histories through application to a previously published DNA-seq/scRNA-seq breast cancer data set. AvailabilityThe source code for TUSV-int is available at https://github.com/CMUSchwartzLab/TUSV-INT.git

cancer biology↗

bioRxiv2023

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming (ILP) based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants (SNV), copy number alterations (CNA) and structural variations (SV) into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness on real data in resolving clonal evolution in the presence of multiple variant types, providing a path towards more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

cancer biology↗

bioRxiv2021

Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

While aligning sequences has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods have much appeal in terms of simplifying the process of inference, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for some emerging forms of data, such as genome skims, which cannot be assembled. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is that they typically rely on simplified models of sequence evolution such as Jukes-Cantor. It is possible to compute pairwise distances under more complex models by computing frequencies of base substitutions provided that these quantities can be estimated in the alignment-free setting. A particular limitation is that for many forms of genomewide data, which arguably present the best use case for alignment-free methods, the strand of DNA sequences is unknown. Under such conditions, the so-called no-strand bias models are the most complex models that can be used. Here, we show how to calculate distances under a no-strain bias restriction of the General Time Reversible (GTR) model called TK4 without relying on alignments. The method relies on replacing letters in the input sequences, and subsequent computation of Jaccard indices between k-mer sets. For the method to work on large genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that these alignment-free distances can be highly accurate when genomes evolve under the assumed models, and we examine the effectiveness of the method on real genomic data.

bioinformatics↗